Overview

We are building production-ready ML solutions and need a Lead Machine Learning Engineer to own end-to-end model delivery and MLOps rigor. You will create forecasting, recommendation, and optimization models, operationalize them with APIs and pipelines, and drive monitoring and continuous improvement—apply now.

Responsibilities

  • Design and build machine learning models for forecasting, classification, recommendation, segmentation and optimization
  • Package models for production use and deliver them through APIs or scheduled jobs
  • Implement monitoring, retraining and lifecycle management for ML solutions
  • Apply MLOps best practices, including model versioning, experiment tracking and reproducible pipelines
  • Track model behavior in production and recommend data-driven improvements
  • Contribute to technical design reviews and present well-reasoned options with trade-offs
  • Document architecture decisions and enable knowledge transfer to internal teams
  • Promote engineering standards, tools and best practices across the team
  • Collaborate with business stakeholders to translate problems into machine learning solutions

Requirements

  • Proven hands-on experience in ML Engineering or Data Engineering for production systems (5+ years)
  • Demonstrated track record of shipping ML models used by real users, including at least 2 live production projects
  • High proficiency in Python, PySpark and SQL
  • Practical skills with Scikit-learn, Databricks (production usage) and Delta Lake
  • Strong expertise with REST APIs, Git, CI/CD pipelines, Docker and Jenkins
  • Working knowledge of MLflow for model versioning and experiment tracking
  • Solid background in time series forecasting, similarity techniques and computer vision models
  • Deep understanding of feature engineering, model evaluation and monitoring
  • Excellent communication skills to partner effectively with non-technical stakeholders
  • Sound judgment to balance model simplicity versus complexity appropriately
  • English proficiency at B2 (Upper-Intermediate) level or higher

Nice to have

  • Experience across retail, fashion, consumer goods or distribution domains
  • Familiarity with enterprise planning tools such as SAP IBP, SAP M3 or SAC
  • Exposure to building model monitoring dashboards using Power BI, Tableau or Looker
  • Knowledge of semantic similarity or embeddings in product catalogs
  • Understanding of multi-country or multi-currency platform challenges
  • Ability to design Lakehouse architectures, including Medallion or Data Mesh

[GTS] Benefits (generic, except India)

  • International projects with top brands
  • Work with global teams of highly skilled, diverse peers
  • Healthcare benefits
  • Employee financial programs
  • Paid time off and sick leave
  • Upskilling, reskilling and certification courses
  • Unlimited access to the LinkedIn Learning library and 22,000+ courses
  • Global career opportunities
  • Volunteer and community involvement opportunities
  • EPAM Employee Groups
  • Award-winning culture recognized by Glassdoor, Newsweek and LinkedIn